Threat actors have continued to bypass advanced security tooling despite the forecast for enterprises worldwide to spend $212 billion on defensive solutions in 2025.* This paradox raises a critical question for CISOs and security leaders: Why isn’t more investment delivering better security outcomes?
The problem is that your security operations center (SOC) is awash in isolated event notifications, creating a ton of noisy data that can easily complicate your work. What's needed is additional context about the environment. Adding context streamlines data analysis for security events, enabling teams to prioritize detected threats and make more efficient decisions about risks.
This is the difference between playing whack-a-mole with event notifications and taking a strategic, thoughtful approach to security operations. Data analytics is the backbone of this approach to SecOps. With a data lake and the application of context, you begin to improve the SOC's responsiveness. Ultimately, situational awareness shifts the SOC from reactive to proactive.
Google Security Operations (formerly Chronicle) has been a trailblazer for modern security operations. Recognized as a Visionary in the 2024 Gartner SIEM Magic Quadrant and a Leader in the IDC MarketScape: Worldwide SIEM for Enterprise 2024, Google has become the security operations platform of choice for many of the world’s leading security teams.
Google Security Operations empowers security teams to thrive with situational awareness through a multi-faceted approach. It delivers context-aware analytics, allowing analysts to understand not just what is happening, but why, by enriching telemetry with entity details like user permissions and asset importance. By comprehensively analyzing data from diverse sources - telemetry, threat intelligence, and vulnerabilities - unified under the Universal Data Model (UDM), it provides a holistic view of the security landscape.
Leveraging AI-driven threat detection, teams can rapidly identify and respond to incidents using integrated investigation and automation tools. Crucially, it integrates Google's and Mandiant's threat intelligence, offering proactive insights into IOCs and TTPs. Finally, enhanced visualization and reporting, including the Graph Investigator, provide clear dashboards and attack timelines, enabling teams to grasp the full scope of threats and make informed decisions swiftly.
A Situational Awareness (SA) Framework is essential for navigating transitions effectively. It encompasses four stages: Perception, Comprehension, Projection, and Resolution. Situational awareness is also a core element of effective decision-making and can reduce the time it takes to identify and respond to critical threats. This article will examine the stages of the framework and contextualize the role that data analytics plays in evolving the SOC into a proactive, risk-focused guardian of critical systems.
The modern security landscape is highly dynamic. Between shifting user behaviors, changing device states, and the potential threat actor compromise, SOC teams are awash in data. This forces many of them to be reactive to security alerts, investigating every possible compromise. Unfortunately, this causes a lot of confusion as the SecOps team is equally likely to investigate a serious threat, such as finding out that Gerald in sales signed up for an AI content-generation tool without permission.
Situational awareness helps SOC teams become proactive instead of reacting to every single alert. Adding operational context through data analytics means that the teams can see that Gerald is performing an unapproved but not malicious action. At the same time, it ensures that teams can identify and respond to malicious actions more accurately.
As one CISO said in our October Executive Roundtable, "Instead of addressing every alert, teams can evaluate threats in context and focus resources where they matter most." This is the power of situational awareness and data analytics. SOC teams are more effective and efficient at protecting the organization when they can better analyze security data.
Data analytics offers the visibility, context, and actionable insights that are needed for SOC teams to make that change.
Regarding situational awareness, there are four main building blocks: Perception, Comprehension, Projection, and Resolution.
Each of these building blocks has a different role in constructing comprehensive situational awareness for security operations teams.
Perception in this context means collecting and aggregating raw data from systems, networks, and endpoints. Strong perception implies that the SOC team has access to all the data types and sources they need to analyze. More intelligence, not less, is required to provide accurate context for prioritizing the SOC's activities because every new piece of data can result in deeper insight.
Data analytics tools facilitate this building block through accurate real-time data collection and normalization. Security tools often gather data in different formats and at various cadences. Data analysis solutions can normalize and transform this information into standard formats, enabling SOC teams with the necessary intelligence.
Instantly standardizing data formats across different security tools.
Correlating disparate alerts to detect broader attack patterns.
Visibility into historical security events for trend analysis.
The ‘Comprehension’ building block relates to understanding data and recognizing patterns. Visualization tools have a major role to play with regard to SOC teams comprehending the data that they're presented with. Think of the ability to map how different endpoints communicate throughout the network or understand the inter-relationship between a network gateway and an employee workstation.
Understanding the full picture of security data in context means that teams can more readily see which security alerts have more impact than others. Data analysis surfaces those hidden patterns, bringing them to light and empowering SOC teams to act.
Map relationships between users, devices, and network activity.
Identifies anomalies that indicate potential cyber threats.
Connects security events to known adversary tactics.
Projection relates to anticipating future threats based on analyzed data and proactively defending assets before they're attacked. This involves looking at historical trends as well as overall behaviors. In fact, behavioral threat detection through examining anomalous activities can be a powerful tool to provide the necessary context of situational awareness.
Artificial intelligence (AI) tools like machine learning-powered data analysis have a significant role in predicting future potential threats. These datasets are often extremely large, and AI agents can be very effective at looking into correlating data points and detecting common behaviors as well as potential connections.
Predictive analytics, when used this way, ultimately enables proactive defense strategies. Think of the power of closing down potential avenues of attack well before threat actors attempt to compromise them in the first place.
Leverages contextual clues to examine anomalous activities
Effectively correlate datapoints across large datasets.
Proactively identifies threats
Resolution, the last building block, refers to acting on insights to mitigate threats and build long-term resilience. These responses are data-driven and analyzed for veracity as the SOC analyzes collected data and prioritizes potential risks for response. SOC teams often leverage automation to respond quickly to threats, improving their ability to remediate issues and address problems quickly.
Data analytics capabilities support these automations and improve incident response. When SOC teams are empowered with a rich organizational context, they can deliver improved outcomes and more readily resolve problems throughout the enterprise. This can often make the difference between surviving the next attack and having it cause a problem.
Enables rapid containment of confirmed threats.
Helps SOC teams allocate resources effectively.
Informs continuous security improvements.
The thing to understand is that automation doesn't replace SOC teams, but rather amplifies their impact by handling the noise that consumes their time otherwise.
The four layers of situational awareness are tightly integrated to elevate SOC capabilities. With data analytics tools, your team can respond to attacks more efficiently and perform their jobs more effectively.
Data collection and aggregation performed in the perception phase are analyzed with context and visualized during the comprehension phase. The projection's predictive modeling and threat anticipation empower the resolution phase's automation and actionable intelligence.
These four pieces are tied together so that each needs to work together. At Netenrich, we regularly enable customers with this sort of analysis capability to help their SOC teams effectively defend the enterprise.
One Netenrich customer recently experienced the power of situational awareness. Faced with skyrocketing costs and operational challenges from its Splunk and XSOAR-based security architecture, Cloud Software Group a global software leader that manages companies like Netscaler, Tibco, and Citrix - sought a more scalable, cost-effective solution to align with its rapid growth and acquisition strategy.
The company's existing SOC platform had become a costly and detrimental bottleneck, resulting in talent retention issues, limited scalability, and quickly increasing costs. They routinely paid high infrastructure costs, exacerbated every time they acquired a new company.
Leveraging Netenrich's Adaptive SecOps, the company achieved more than a 50% reduction in security operational expenses, enhanced its visibility across its business units, and streamlined its security processes. Netenrich implemented Google’s Universal Data Model to unify data ingestion, normalize logs from disparate sources, and eliminate blind spots.
The transformation delivered enhanced threat detection efficacy, faster response times, and a proactive security posture, positioning the company for long-term scalability and operational excellence.
Netenrich successfully advanced its client's capabilities to monitor and react to potential threats while gaining insight into the most important information. After Netenrich implemented its solution, the team could access the data needed to gain the context required to act effectively.
Data analytics enhances the situational awareness of the modern SecOps professional. The building blocks of gaining this contextual insight into the organization - Perception, Comprehension, Projection, and Resolution - are necessary to understand for organizations to gain the whole picture and effectively respond to complicated security threats.
Part and parcel of enabling these capabilities is having a strong data foundation and ingesting security event data into a single source like a data lake for later analysis and transformation. The data lake ensures that organizations don't miss any critical data and can more effectively unify intelligence later for enhanced operational awareness.
Security leaders must ask: Are we simply collecting data-or transforming it into intelligence?
A data lake approach enables security teams to ingest, process, and analyze threat intelligence at scale, unlocking the full potential of situational awareness.
Yet, too often, data lakes become passive storage rather than engines for real-time security intelligence. The key isn’t just gathering data-it’s connecting the dots to shift from reactive defense to proactive risk management.
Are you engineering your security operations to harness the full potential of data analytics - or just storing logs and hoping for the best?
To find out more, we encourage you to check out Netenrich today.
Sources: * Gartner: $212 billion on defensive solutions in 2025.